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In this course, students apply a wide range of data analytical methods and tools covered in the whole program. This includes in particular time series analysis, multivariate analysis, predictive modelling, quality control, regression, machine learning, data visualisation, experimental design and optimisation. Computation tools include in particular querying language (SQL), R, Python, Matlab, SAS and SPSS.

Example 1: Data Visualisation project. A train link company was interested in how the on-time running of the networks can be best visualised, and where the pinch points are in the networks. Our students utilised general data visualisation and geospatial data visualisation tools to help industry partners locating the worst performing services and if some services have to be removed.

Example 2: Water Utility project, one of the largest water supply companies in Melbourne, has a yearly maintenance program for sewer reticulation cleaning including key customers and key events. The Manhole gas check maintenance program is an annual program. the company was interested in finding out how effective these programs are, that is, how often these reticulation lines and manholes report a blockage after cleaning, and if the frequency of blockages in these assets has come down as a result of preventative maintenance programs. Our students deciphered whether prevention programs reduce the need for responses by making use of multivariate analysis of variance techniques. Time-to-failure analyses highlighted whether prevention programs can extend the time before a failure is seen.

Example 3: Customer Segmentation for Supermarkets, one of the largest supermarket chains in Australia. Our students have built a customer segmentation model that will be used by sales to segment their fresh produce customers based on behaviour, types of products and amounts of products purchased. The project aims to understand customer behaviour, and therefore help Coles for future promotions at their target market.